There are different embedding distributions in the source and target domain, as shown in Fig. 1. The domain shift results in that the knowledge of domain A can not work well on domain B. A typical method to address this problem is aligning the two distributions by adversarial training [1]}, domain alignment [2]} and knowledge distillation [3]}. They usually choose some samples from the target domain as an auxiliary set during training. Obviously, these approaches violate the CD-FSL setting, on which the target domain is absolutely unknown during the training on the source domain. Therefore, FT [4]} explores to realize feature transformation without access to the target domain, which employs affine transforms to simulate various feature distributions under different domains. This transformation is not directed to the target domain, which performs unstably on the different target domains. How to employ limited labeled samples to provide a directive metric space for the target source is a research focus in CD-FSL.
